Statistics by subject – Statistical methods

Other available resources to support your research.

Help for sorting results
Browse our central repository of key standard concepts, definitions, data sources and methods.
Loading
Loading in progress, please wait...
All (20)

All (20) (20 of 20 results)

  • Articles and reports: 12-001-X199400214419
    Description:

    The study was undertaken to evaluate some alternative small areas estimators to produce level estimates for unplanned domains from the Italian Labour Force Sample Survey. In our study, the small areas are the Health Service Areas, which are unplanned sub-regional territorial domains and were not isolated at the time of sample design and thus cut across boundaries of the design strata. We consider the following estimators: post-stratified ratio, synthetic, composite expressed as linear combination of synthetic and of post-stratified ratio, and sample size dependent. For all the estimators considered in this study, the average percent relative biases and the average relative mean square errors were obtained in a Monte Carlo study in which the sample design was simulated using data from the 1981 Italian Census.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214422
    Description:

    Dual system estimation (DSE) has been used since 1950 by the U.S. Bureau of Census for coverage evaluation of the decennial census. In the DSE approach, data from a sample is combined with data from the census to estimate census undercount and overcount. DSE relies upon the assumption that individuals in both the census and the sample can be matched perfectly. The unavoidable mismatches and erroneous nonmatches reduce the accuracy of the DSE. This paper reconsiders the DSE approach by relaxing the perfect matching assumption and proposes models to describe two types of matching errors, false matches of nonmatching cases and false nonmatches of matching cases. Methods for estimating population total and census undercount are presented and illustrated using data from 1986 Los Angeles test census and 1990 Decennial Census.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214417
    Description:

    Without-replacement list sampling with probability proportional to some measure of element size has not enjoyed much application in forestry because of the difficulty of implementing such sample strategies, that have been termed \pi ps designs to distinguish without-replacement sampling from the well-known with-replacement pps designs. In this contribution, an exact \pi ps strategy (Sunter’s variant 2), an approximate \pi ps design (Sunter’s variant 1) and the Rao-Hartley-Cochran random group method are examined and the variances of the respective estimators for total bole volume are computed for four tree populations. The results indicate that compared to the Rao-Hartley-Cochran design Sunter’s variant 1 in general leads to higher precision if the relationship between auxiliary information x_k and target characteristic y_k is loose but is sensitive to the ordering of the sampling frame, whereas the Rao-Hartley-Cochran design does not require the sampling frame to be ordered at all and appears to be superior if strong linear relationships between x_k and y_k are present.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214423
    Description:

    Most surveys suffer from the problem of missing data caused by nonresponse. To deal with this problem, imputation is often used to create a “completed data set”, that is, a data set composed of actual observations (for the respondents) and imputations (for the nonrespondents). Usually, imputation is carried out under the assumption of unconfounded response mechanism. When this assumption does not hold, a bias is introduced in the standard estimator of the population mean calculated from the completed data set. In this paper, we pursue the idea of using simple correction factors for the bias problem in the case that ratio imputation is used. The effectiveness of the correction factors is studied by Monte Carlo simulation using artificially generated data sets representing various super-populations, nonresponse rates, nonresponse mechanisms, and correlations between the variable of interest and the auxiliary variable. These correction factors are found to be effective especially when the population follows the model underlying ratio imputation. An option for estimating the variance of the corrected point estimates is also discussed.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214427
    Description:

    A generalized regression estimator for domains and an approximate estimator of its variance are derived under two-phase sampling for stratification with Poisson selection at each phase. The derivations represent an application of the general framework for regression estimation for two-phase sampling developed by Särndal and Swensson (1987) and Särndal, Swensson and Wretman (1992). The empirical efficiency of the generalized regression estimator is examined using data from Statistics Canada’s annual two-phase sample of tax records. Three particular cases of the generalized regression estimator - two regression estimators and a poststratified estimator - are compared to the Horvitz-Thompson estimator.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214426
    Description:

    In the MARS Project (Monitoring Agriculture with Remote Sensing) of the E.C. (European Community), area frames based on a square grid are used for area estimation through ground surveys and high resolution satellite images. These satellite images are useful, though expensive, for area estimation: their use for yield estimation is not yet operational. To fill this gap the sample elements (segments) of the area survey are used as well for sampling farms with a template of points overlaid on the segment. Most often we use a fixed number of points per segment. Farmers are asked to provide global data for the farm, and estimates are computed with a Horvitz-Thompson approach. Major problems include locating farmers and checking for misunderstanding of instructions. Good results are obtained for area and for production of the main crops. Area frames need to be complemented with list frames (multiple frames) to give reliable estimates for livestock.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214418
    Description:

    We deal with the nonresponse problem by drawing on the model of selection in phases that was proposed by Särndal and Swenson (1987). To estimate response probabilities, we use the nonparametric approach first advanced by Giommi (1987). We define estimators according to the nonparametric estimation (NPE) model, and we study their general properties empirically. Inference is based on the concept of quasi-randomization (Oh and Scheuren 1983). The emphasis is on estimating the variance and constructing confidence intervals. We find, by way of a Monte Carlo study, that it is possible to improve the quality of the estimators considered by using a variant of the NPE approach. The latter also serves to confirm the performance of regression estimators in terms of variance estimation.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214420
    Description:

    The statistical literature contains many methods for disclosure limitation in microdata. However, their use by statistical agencies and understanding of their properties and effects has been limited. For purposes of furthering research and use of these methods, and facilitating their evaluation and quality assurance, it would be desirable to formulate them within a single framework. A framework called matrix masking - based on ordinary matrix arithmetic - is presented, and explicit matrix mask formulations are given for the principal microdata disclosure limitation methods in current use. This enables improved understanding and implementation of these methods by statistical agencies and other practitioners.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214421
    Description:

    This paper discusses testing a single hypothesis about linear regression coefficients based on sample survey data. It suggests that when the design-based linearization variance estimator for a regression coefficient is used it should be adjusted to reduce its slight model bias and that a Satterthwaite-like estimation of its effective degrees of freedom be made. A very important special case of this analysis is its application to domain means.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214425
    Description:

    We present a formal model based sampling solution to the problem of estimating list frame size based on capture-recapture sampling which has been widely used for animal populations and for adjusting the US census. For two incomplete lists it is easy to estimate total frame size using the Lincoln-Petersen estimator. This estimator is model based with a key assumption being independence of the two lists. Once an estimator of the population (frame) size has been obtained it is possible to obtain an estimator of a population total for some characteristic if a sample of units has that characteristic measured. A discussion of the properties of this estimator will be presented. An example where the establishments are fishing boats taking part in an ocean fishery off the Atlantic Coast of the United States is presented. Estimation of frame size and then population totals using a capture-recapture model is likely to have broad application in establishment surveys due to practicality and cost savings but possible biases due to assumption violations need to be considered.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214424
    Description:

    This paper provides an overview of important considerations that should be taken into account when developing and designing questionnaires for business surveys. These considerations include the determination of objectives and data requirements, consultation with data users and respondents, and methods for testing questionnaires. In developing and designing business survey questionnaires, focus groups and cognitive research methods help the researcher to identify potential sources of measurement error and to understand the response process that respondents go through in completing the questionnaires. Examples of focus groups and cognitive research undertaken by Statistics Canada are provided.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400114432
    Description:

    Two sampling strategies for estimation of population mean in overlapping clusters with known population size have been proposed by Singh (1988). In this paper, ratio estimators under these two strategies are studied assuming the actual population size to be unknown, which is the more realistic situation in sample surveys. The sampling efficiencies of the two strategies are compared and a numerical illustration is provided.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114429
    Description:

    A regression weight generation procedure is applied to the 1987-1988 Nationwide Food Consumption Survey of the U.S. Department of Agriculture. Regression estimation was used because of the large nonresponse in the survey. The regression weights are generalized least squares weights modified so that all weights are positive and so that large weights are smaller than the least squares weights. It is demonstrated that the regression estimator has the potential for large reductions in mean square error relative to the simple direct estimator in the presence of nonresponse.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114430
    Description:

    Rao and Nigam (1990, 1992) showed how a class of controlled sampling designs can be implemented using linear programming. In this article their approach is applied to multi-way stratification. A comparison is made with existing methods both by illustrating the sampling schemes generated for specific examples and by evaluating mean squared errors. The proposed approach is relatively simple to use and appears to have reasonable mean squared error properties. The computations required can, however, increase rapidly as the number of cells in the multi-way classification increase. Variance estimation is also considered.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114436
    Description:

    This paper identifies some technical issues in the provision of small area data derived from censuses, administrative records and surveys. Although the issues are of a general nature, they are discussed in the context of programs at Statistics Canada. For survey-based estimates, the need for developing an overall strategy is stressed and salient features of survey design that have an impact on small area data are highlighted in the context of redesigning a household survey. A brief review of estimation methods with their strengths and weaknesses is also presented.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114431
    Description:

    The Random Group Method for sampling with probability proportional to size (PPS) is extended to sampling over two occasions. Information on a study variate observed on the first occasion is used to select the matched portion of the sample on the second occasion. Two real data sets are considered for numerical illustration and for comparison with other existing methods.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114433
    Description:

    Imputation is a common technique employed by survey-taking organizations in order to address the problem of item nonresponse. While in most of the cases the resulting completed data sets provide good estimates of means and totals, the corresponding variances are often grossly underestimated. A number of methods to remedy this problem exists, but most of them depend on the sampling design and the imputation method. Recently, Rao (1992), and Rao and Shao (1992) have proposed a unified jackknife approach to variance estimation of imputed data sets. The present paper explores this technique empirically, using a real population of businesses, under a simple random sampling design and a uniform nonresponse mechanism. Extensions to stratified multistage sample designs are considered, and the performance of the proposed variance estimator under non-uniform response mechanisms is briefly investigated.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114434
    Description:

    In estimation for small areas it is common to borrow strength from other small areas since the direct survey estimates often have large sampling variability. A class of methods called composite estimation addresses the problem by using a linear combination of direct and synthetic estimators. The synthetic component is based on a model which connects small area means cross-sectionally (over areas) and/or over time. A cross-sectional empirical best linear unbiased predictor (EBLUP) is a composite estimator based on a linear regression model with small area effects. In this paper we consider three models to generalize the cross-sectional EBLUP to use data from more than one time point. In the first model, regression parameters are random and serially dependent but the small area effects are assumed to be independent over time. In the second model, regression parameters are nonrandom and may take common values over time but the small area effects are serially dependent. The third model is more general in that regression parameters and small area effects are assumed to be serially dependent. The resulting estimators, as well as some cross-sectional estimators, are evaluated using bi-annual data from Statistics Canada’s National Farm Survey and January Farm Survey.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114435
    Description:

    The problem of estimating domain totals and means from sample survey data is common. When the domain is large, the observed sample is generally large enough that direct, design-based estimators are sufficiently accurate. But when the domain is small, the observed sample size is small and direct estimators are inadequate. Small area estimation is a particular case in point and alternative methods such as synthetic estimation or model-based estimators have been developed. The two usual facets of such methods are that information is ‘borrowed’ from other small domains (or areas) so as to obtain more precise estimators of certain parameters and these are then combined with auxiliary information, such as population means or totals, from each small area in turn to obtain a more precise estimate of the domain (or area) mean or total. This paper describes a case involving unequal probability sampling in which no auxiliary population means or totals are available and borrowing strength from other domains is not allowed and yet simple model-based estimators are developed which appear to offer substantial efficiency gains. The approach is motivated by an application to market research but the methods are more widely applicable.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114428
    Description:

    Recently, much effort has been directed towards counting and characterizing the homeless. Most of this work, however, has focused on homeless persons in urban areas. In this paper, we describe efforts to estimate the rate of homelessness in nonurban counties in Ohio. The methods for locating homeless persons and even the definition of homelessness are different in rural areas where there are fewer institutions for sheltering and feeding the homeless. There may also be a problem with using standard survey sampling estimators, which typically require large population sizes, large sample sizes, and small sampling fractions. We describe a survey of homeless persons in nonurban Ohio and present a simulation study to assess the usefulness of standard estimators for a population proportion from a stratified cluster sample.

    Release date: 1994-06-15

Data (0)

Data (0) (0 results)

Your search for "" found no results in this section of the site.

You may try:

Analysis (20)

Analysis (20) (20 of 20 results)

  • Articles and reports: 12-001-X199400214419
    Description:

    The study was undertaken to evaluate some alternative small areas estimators to produce level estimates for unplanned domains from the Italian Labour Force Sample Survey. In our study, the small areas are the Health Service Areas, which are unplanned sub-regional territorial domains and were not isolated at the time of sample design and thus cut across boundaries of the design strata. We consider the following estimators: post-stratified ratio, synthetic, composite expressed as linear combination of synthetic and of post-stratified ratio, and sample size dependent. For all the estimators considered in this study, the average percent relative biases and the average relative mean square errors were obtained in a Monte Carlo study in which the sample design was simulated using data from the 1981 Italian Census.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214422
    Description:

    Dual system estimation (DSE) has been used since 1950 by the U.S. Bureau of Census for coverage evaluation of the decennial census. In the DSE approach, data from a sample is combined with data from the census to estimate census undercount and overcount. DSE relies upon the assumption that individuals in both the census and the sample can be matched perfectly. The unavoidable mismatches and erroneous nonmatches reduce the accuracy of the DSE. This paper reconsiders the DSE approach by relaxing the perfect matching assumption and proposes models to describe two types of matching errors, false matches of nonmatching cases and false nonmatches of matching cases. Methods for estimating population total and census undercount are presented and illustrated using data from 1986 Los Angeles test census and 1990 Decennial Census.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214417
    Description:

    Without-replacement list sampling with probability proportional to some measure of element size has not enjoyed much application in forestry because of the difficulty of implementing such sample strategies, that have been termed \pi ps designs to distinguish without-replacement sampling from the well-known with-replacement pps designs. In this contribution, an exact \pi ps strategy (Sunter’s variant 2), an approximate \pi ps design (Sunter’s variant 1) and the Rao-Hartley-Cochran random group method are examined and the variances of the respective estimators for total bole volume are computed for four tree populations. The results indicate that compared to the Rao-Hartley-Cochran design Sunter’s variant 1 in general leads to higher precision if the relationship between auxiliary information x_k and target characteristic y_k is loose but is sensitive to the ordering of the sampling frame, whereas the Rao-Hartley-Cochran design does not require the sampling frame to be ordered at all and appears to be superior if strong linear relationships between x_k and y_k are present.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214423
    Description:

    Most surveys suffer from the problem of missing data caused by nonresponse. To deal with this problem, imputation is often used to create a “completed data set”, that is, a data set composed of actual observations (for the respondents) and imputations (for the nonrespondents). Usually, imputation is carried out under the assumption of unconfounded response mechanism. When this assumption does not hold, a bias is introduced in the standard estimator of the population mean calculated from the completed data set. In this paper, we pursue the idea of using simple correction factors for the bias problem in the case that ratio imputation is used. The effectiveness of the correction factors is studied by Monte Carlo simulation using artificially generated data sets representing various super-populations, nonresponse rates, nonresponse mechanisms, and correlations between the variable of interest and the auxiliary variable. These correction factors are found to be effective especially when the population follows the model underlying ratio imputation. An option for estimating the variance of the corrected point estimates is also discussed.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214427
    Description:

    A generalized regression estimator for domains and an approximate estimator of its variance are derived under two-phase sampling for stratification with Poisson selection at each phase. The derivations represent an application of the general framework for regression estimation for two-phase sampling developed by Särndal and Swensson (1987) and Särndal, Swensson and Wretman (1992). The empirical efficiency of the generalized regression estimator is examined using data from Statistics Canada’s annual two-phase sample of tax records. Three particular cases of the generalized regression estimator - two regression estimators and a poststratified estimator - are compared to the Horvitz-Thompson estimator.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214426
    Description:

    In the MARS Project (Monitoring Agriculture with Remote Sensing) of the E.C. (European Community), area frames based on a square grid are used for area estimation through ground surveys and high resolution satellite images. These satellite images are useful, though expensive, for area estimation: their use for yield estimation is not yet operational. To fill this gap the sample elements (segments) of the area survey are used as well for sampling farms with a template of points overlaid on the segment. Most often we use a fixed number of points per segment. Farmers are asked to provide global data for the farm, and estimates are computed with a Horvitz-Thompson approach. Major problems include locating farmers and checking for misunderstanding of instructions. Good results are obtained for area and for production of the main crops. Area frames need to be complemented with list frames (multiple frames) to give reliable estimates for livestock.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214418
    Description:

    We deal with the nonresponse problem by drawing on the model of selection in phases that was proposed by Särndal and Swenson (1987). To estimate response probabilities, we use the nonparametric approach first advanced by Giommi (1987). We define estimators according to the nonparametric estimation (NPE) model, and we study their general properties empirically. Inference is based on the concept of quasi-randomization (Oh and Scheuren 1983). The emphasis is on estimating the variance and constructing confidence intervals. We find, by way of a Monte Carlo study, that it is possible to improve the quality of the estimators considered by using a variant of the NPE approach. The latter also serves to confirm the performance of regression estimators in terms of variance estimation.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214420
    Description:

    The statistical literature contains many methods for disclosure limitation in microdata. However, their use by statistical agencies and understanding of their properties and effects has been limited. For purposes of furthering research and use of these methods, and facilitating their evaluation and quality assurance, it would be desirable to formulate them within a single framework. A framework called matrix masking - based on ordinary matrix arithmetic - is presented, and explicit matrix mask formulations are given for the principal microdata disclosure limitation methods in current use. This enables improved understanding and implementation of these methods by statistical agencies and other practitioners.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214421
    Description:

    This paper discusses testing a single hypothesis about linear regression coefficients based on sample survey data. It suggests that when the design-based linearization variance estimator for a regression coefficient is used it should be adjusted to reduce its slight model bias and that a Satterthwaite-like estimation of its effective degrees of freedom be made. A very important special case of this analysis is its application to domain means.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214425
    Description:

    We present a formal model based sampling solution to the problem of estimating list frame size based on capture-recapture sampling which has been widely used for animal populations and for adjusting the US census. For two incomplete lists it is easy to estimate total frame size using the Lincoln-Petersen estimator. This estimator is model based with a key assumption being independence of the two lists. Once an estimator of the population (frame) size has been obtained it is possible to obtain an estimator of a population total for some characteristic if a sample of units has that characteristic measured. A discussion of the properties of this estimator will be presented. An example where the establishments are fishing boats taking part in an ocean fishery off the Atlantic Coast of the United States is presented. Estimation of frame size and then population totals using a capture-recapture model is likely to have broad application in establishment surveys due to practicality and cost savings but possible biases due to assumption violations need to be considered.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400214424
    Description:

    This paper provides an overview of important considerations that should be taken into account when developing and designing questionnaires for business surveys. These considerations include the determination of objectives and data requirements, consultation with data users and respondents, and methods for testing questionnaires. In developing and designing business survey questionnaires, focus groups and cognitive research methods help the researcher to identify potential sources of measurement error and to understand the response process that respondents go through in completing the questionnaires. Examples of focus groups and cognitive research undertaken by Statistics Canada are provided.

    Release date: 1994-12-15

  • Articles and reports: 12-001-X199400114432
    Description:

    Two sampling strategies for estimation of population mean in overlapping clusters with known population size have been proposed by Singh (1988). In this paper, ratio estimators under these two strategies are studied assuming the actual population size to be unknown, which is the more realistic situation in sample surveys. The sampling efficiencies of the two strategies are compared and a numerical illustration is provided.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114429
    Description:

    A regression weight generation procedure is applied to the 1987-1988 Nationwide Food Consumption Survey of the U.S. Department of Agriculture. Regression estimation was used because of the large nonresponse in the survey. The regression weights are generalized least squares weights modified so that all weights are positive and so that large weights are smaller than the least squares weights. It is demonstrated that the regression estimator has the potential for large reductions in mean square error relative to the simple direct estimator in the presence of nonresponse.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114430
    Description:

    Rao and Nigam (1990, 1992) showed how a class of controlled sampling designs can be implemented using linear programming. In this article their approach is applied to multi-way stratification. A comparison is made with existing methods both by illustrating the sampling schemes generated for specific examples and by evaluating mean squared errors. The proposed approach is relatively simple to use and appears to have reasonable mean squared error properties. The computations required can, however, increase rapidly as the number of cells in the multi-way classification increase. Variance estimation is also considered.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114436
    Description:

    This paper identifies some technical issues in the provision of small area data derived from censuses, administrative records and surveys. Although the issues are of a general nature, they are discussed in the context of programs at Statistics Canada. For survey-based estimates, the need for developing an overall strategy is stressed and salient features of survey design that have an impact on small area data are highlighted in the context of redesigning a household survey. A brief review of estimation methods with their strengths and weaknesses is also presented.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114431
    Description:

    The Random Group Method for sampling with probability proportional to size (PPS) is extended to sampling over two occasions. Information on a study variate observed on the first occasion is used to select the matched portion of the sample on the second occasion. Two real data sets are considered for numerical illustration and for comparison with other existing methods.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114433
    Description:

    Imputation is a common technique employed by survey-taking organizations in order to address the problem of item nonresponse. While in most of the cases the resulting completed data sets provide good estimates of means and totals, the corresponding variances are often grossly underestimated. A number of methods to remedy this problem exists, but most of them depend on the sampling design and the imputation method. Recently, Rao (1992), and Rao and Shao (1992) have proposed a unified jackknife approach to variance estimation of imputed data sets. The present paper explores this technique empirically, using a real population of businesses, under a simple random sampling design and a uniform nonresponse mechanism. Extensions to stratified multistage sample designs are considered, and the performance of the proposed variance estimator under non-uniform response mechanisms is briefly investigated.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114434
    Description:

    In estimation for small areas it is common to borrow strength from other small areas since the direct survey estimates often have large sampling variability. A class of methods called composite estimation addresses the problem by using a linear combination of direct and synthetic estimators. The synthetic component is based on a model which connects small area means cross-sectionally (over areas) and/or over time. A cross-sectional empirical best linear unbiased predictor (EBLUP) is a composite estimator based on a linear regression model with small area effects. In this paper we consider three models to generalize the cross-sectional EBLUP to use data from more than one time point. In the first model, regression parameters are random and serially dependent but the small area effects are assumed to be independent over time. In the second model, regression parameters are nonrandom and may take common values over time but the small area effects are serially dependent. The third model is more general in that regression parameters and small area effects are assumed to be serially dependent. The resulting estimators, as well as some cross-sectional estimators, are evaluated using bi-annual data from Statistics Canada’s National Farm Survey and January Farm Survey.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114435
    Description:

    The problem of estimating domain totals and means from sample survey data is common. When the domain is large, the observed sample is generally large enough that direct, design-based estimators are sufficiently accurate. But when the domain is small, the observed sample size is small and direct estimators are inadequate. Small area estimation is a particular case in point and alternative methods such as synthetic estimation or model-based estimators have been developed. The two usual facets of such methods are that information is ‘borrowed’ from other small domains (or areas) so as to obtain more precise estimators of certain parameters and these are then combined with auxiliary information, such as population means or totals, from each small area in turn to obtain a more precise estimate of the domain (or area) mean or total. This paper describes a case involving unequal probability sampling in which no auxiliary population means or totals are available and borrowing strength from other domains is not allowed and yet simple model-based estimators are developed which appear to offer substantial efficiency gains. The approach is motivated by an application to market research but the methods are more widely applicable.

    Release date: 1994-06-15

  • Articles and reports: 12-001-X199400114428
    Description:

    Recently, much effort has been directed towards counting and characterizing the homeless. Most of this work, however, has focused on homeless persons in urban areas. In this paper, we describe efforts to estimate the rate of homelessness in nonurban counties in Ohio. The methods for locating homeless persons and even the definition of homelessness are different in rural areas where there are fewer institutions for sheltering and feeding the homeless. There may also be a problem with using standard survey sampling estimators, which typically require large population sizes, large sample sizes, and small sampling fractions. We describe a survey of homeless persons in nonurban Ohio and present a simulation study to assess the usefulness of standard estimators for a population proportion from a stratified cluster sample.

    Release date: 1994-06-15

Reference (0)

Reference (0) (0 results)

Your search for "" found no results in this section of the site.

You may try:

Date modified: